346 research outputs found
Steganography: a class of secure and robust algorithms
This research work presents a new class of non-blind information hiding
algorithms that are stego-secure and robust. They are based on some finite
domains iterations having the Devaney's topological chaos property. Thanks to a
complete formalization of the approach we prove security against watermark-only
attacks of a large class of steganographic algorithms. Finally a complete study
of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph
Recent Advances in Steganography
Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced
Evaluation of transform based image coders, using different transforms and techniques in the transform domain
This paper addresses the most relevant aspects of lossy image coding techniques, and presents an
evaluation study on this subject, using several transforms and different methods in the transform domain. We
developed different transform based image coders/decoders (codecs) using different transforms, such as the
discrete cosine transform, the discrete wavelet transform and the S transform. Besides JPEG Baseline, we also
use other techniques and methods in the transform domain such as a DWT based JPEG-like (JPEG DWT), a
JPEG DWT with visual threshold (JPEG-VT), a JPEGâlike coder based on the ST, and an EZW coder. The
codecs were programmed in MATLABâą, using custom and built-in functions. The structures of the codecs
are presented, also as some experimental results which allow us evaluate them, and support this study
Selecting Low-level Features for Image Quality Assessment by Statistical Methods
Image quality assessment is animportant component in every image processingsystem where the last link of the chain is thehuman observer. This domain is of increasinginterest, in particular in the context of imagecompression where coding scheme optimizationis based on the distortion measure. Manyobjective image quality measures have beenproposed in the literature and validated bycomparing them to the Mean Opinion Score(MOS). We propose in this paper an empiricalstudy of several indicators and show how onecan improve the performances by combiningthem. We learn a regularized regression modeland apply variable selection techniques toautomatically find the most relevant indicators.Our technique enhances the state of the artresults on two publicly available databases
Quality criteria benchmark for hyperspectral imagery
Hyperspectral data appear to be of a growing interest
over the past few years. However, applications for hyperspectral
data are still in their infancy as handling the significant size of
the data presents a challenge for the user community. Efficient
compression techniques are required, and lossy compression,
specifically, will have a role to play, provided its impact on remote
sensing applications remains insignificant. To assess the data
quality, suitable distortion measures relevant to end-user applications
are required. Quality criteria are also of a major interest
for the conception and development of new sensors to define their
requirements and specifications. This paper proposes a method to
evaluate quality criteria in the context of hyperspectral images.
The purpose is to provide quality criteria relevant to the impact
of degradations on several classification applications. Different
quality criteria are considered. Some are traditionnally used in
image and video coding and are adapted here to hyperspectral
images. Others are specific to hyperspectral data.We also propose
the adaptation of two advanced criteria in the presence of different
simulated degradations on AVIRIS hyperspectral images. Finally,
five criteria are selected to give an accurate representation of the
nature and the level of the degradation affecting hyperspectral
data
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Reduced reference image and video quality assessments: review of methods
With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications
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